Took Code Quality to the Next Level with Grafana Dashboards — Dynamic Over Static!

Title: Visualizing Code Quality Trends Using Jenkins, Python, InfluxDB, and Grafana

In large-scale projects, tracking code quality metrics like static analysis and code coverage can be challenging. Typically, Jenkins pipelines are used to run dynamic and static code analysis, generating detailed reports. However, navigating these lengthy reports for every project and build is time-consuming, and providing Jenkins access to all users can be risky.

To overcome this, I developed a solution that centralizes and visualizes code quality metrics. By integrating Python, InfluxDB, and Grafana, I can track and display key trends such as branch coverage, statement coverage, and static analysis results. This eliminates the need for direct Jenkins access and manual report analysis, offering a powerful, data-driven overview of the project’s quality over time.

Key Benefits

• Centralized Dashboard: Code quality trends for all projects and builds in one place.
  - No Jenkins Access: Developers and stakeholders don’t need Jenkins access to view code metrics.
  - Automated Process: Automatically fetch, parse, and store quality data after every build.
  - Visual Insights: Grafana provides a visual, easy-to-read representation of code quality trends over time.
• Step-by-Step Guide to Achieving This

1. Set up Jenkins Pipelines to Run Code Quality Analysis

In your Jenkins pipeline, run both static and dynamic code analysis using tools like `pylint`, `pytest-cov`, or `SonarQube`. Ensure these tools generate reports in formats like JUnit XML or JSON.

```groovy
pipeline {
 stages {
 stage('Code Quality Analysis') {
 steps {
 script {
 sh 'pylint myscript.py - output-format=json > pylint-report.json'
 sh 'pytest - cov=myscript - cov-report=xml > coverage-report.xml'
 }
 }
 }
 }
}
```

2. Create Python Scripts to Parse Reports

Write Python scripts that can parse the generated reports (e.g., JUnit XML, JSON) and extract metrics like:

- Branch coverage
- Statement coverage
- Static code analysis issues (like pylint score)

3. Send Data to InfluxDB

Once the Python scripts extract the relevant metrics, push them to InfluxDB using HTTP requests. Each build can send its metrics with project identifiers for easy aggregation in InfluxDB.

Ensure your InfluxDB schema is designed to store key code quality metrics for each project and build.

4. Set Up Grafana to Visualize Data

In Grafana, create dashboards that visualize the collected metrics from InfluxDB. This could include:

- Time-series charts showing code coverage trends.
- Heatmaps highlighting areas of the code with the most static analysis issues.
- Tables or bar charts showing the pylint score or coverage percentage per build.

Example InfluxDB query for Grafana:

SELECT mean("pylint_score") FROM "code_quality" WHERE "project" = 'my_project' GROUP BY time(1d)

5. Automate the Process in Jenkins

To automate the entire process after each build, integrate the Python script execution into your Jenkins pipeline. This ensures that every time code analysis is run, the results are immediately sent to InfluxDB.

pipeline {
 stages {
 stage('Parse and Send Metrics') {
 steps {
 script {
 sh 'python3 parse_metrics.py'
 }
 }
 }
 }
}

6. Monitor Code Quality Trends in Grafana

With all data stored in InfluxDB, use Grafana to monitor your project’s code quality trends over time. This provides real-time insights into code health, allowing teams to address issues quickly and improve overall quality.

Conclusion

By combining Jenkins pipelines, Python scripts, InfluxDB, and Grafana, you can streamline code quality monitoring across your projects. This solution eliminates the need for cumbersome report analysis and provides clear, actionable insights into your project’s health. It’s a scalable, automated, and user-friendly approach that empowers developers to focus on improving code, rather than sifting through reports.

You can find the full code and implementation details in the GitHub repo , or follow my journey on Medium for more DevOps tips and tutorials.